Name
Abstract | ||
|---|---|---|
DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. Its design departs from most other existing frameworks in that it seeks to make algorithms explicit and data structures transparent, as opposed to the more common black-box frameworks. Freely available with extensive documentation at http://deap.gel.ulaval.ca, DEAP is an open source project under an LGPL license. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.5555/2503308.2503311 | Journal of Machine Learning Research |
Keywords | Field | DocType |
open source project,lgpl license,extensive documentation,existing framework,evolutionary algorithm,common black-box framework,novel evolutionary computation framework,design departs,data structure,rapid prototyping | Rapid prototyping,Evolutionary algorithm,Computer science,Theoretical computer science,Artificial intelligence,License,Data structure,Software engineering,Distributed evolutionary algorithms,Evolutionary computation,DEAP,Documentation,Machine learning | Journal |
Volume | Issue | ISSN |
13 | 1 | 1532-4435 |
Citations | PageRank | References |
168 | 5.74 | 3 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Félix-Antoine Fortin | 1 | 230 | 9.07 |
| François-Michel De Rainville | 2 | 199 | 9.27 |
| Marc-André Gardner | 3 | 222 | 11.20 |
| Marc Parizeau | 4 | 811 | 87.35 |
| Christian Gagné | 5 | 627 | 52.38 |